Method of quantitative analysis

ABSTRACT

A system and method for the identification, analysis, attribution, and graphical display pertaining to the effectiveness of public relations is described. The methodology is based on a massively quantitative approach suitable for numerical processing. This method provides a computer-based means of consolidating both internal and external data and producing a graphical representation of the quantitative results to attribute individual contributions of separate data sources.

BACKGROUND

The invention pertains to the attribution to and effectiveness of single known data points and their effect on other single, but unknown data points, in a massive secondary, tertiary or higher linked system. One example of this is the attribution to and effectiveness of public relations or other marketing events on revenue. It is known qualitatively that public reputation and consumer awareness are key drivers of corporate revenue and brand value. However, the effects of particular public relations levers or marketing events on the reputation or awareness of a firm is difficult to attribute quantitatively.

Traditionally, public relations and marketing professionals analyze paper or electronic sources to determine what effect, if any, their efforts to drive company reputation and consumer awareness through the media can have any measurable effect. And any subsequent measured effects deduced has been limited to a narrow set of metrics such as share-of-voice, number of impressions, etc. No further conclusions have been made to quantitatively link these already limited set of metrics and its effect on sales or revenue-the ultimate measure of corporate health.

Whereas earlier ages were hampered by the lack of paper sources, current analysts may be overwhelmed by the amount of data that are electronically available measuring media performance through digital platforms such as search engines or third-party aggregated press databases. This information overload has made it harder, rather than easier to determine the cause and effect of public relations and marketing efforts and the effect of reputation and public awareness campaigns have in driving corporate revenue. Further, current methods of measuring public relations and marketing performance have been limited to efficiencies on a per-impression acquisition or per-campaign basis. For example, cost per impression or cost per click-through on various media channels. A company with a far larger market share (or indeed PR and advertising budget) will naturally have wider media exposure than a smaller competitor, yet this in itself does not quantitatively indicate how effectively the available resources to impart the consumer awareness in the media are being utilized as a contributor to corporate revenue. There is a need, fulfilled by this invention, to resolve this massive data dump into coherent, graphical results.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a method to use statistical modeling to establish visual, comparative and predictive relationships between any data sets.

It is an object of the present invention to provide a method of performance measurement for public relations, marketing and advertising events.

It is an object of the present invention to provide a method for consolidating information regarding events for evaluation of corporate key performance relevance from public relations, marketing and advertising events.

It is an object of the present invention to provide a method to integrate and compare on an equivalent basis the effectiveness of a variety of public relations, advertising and marketing levers.

It is a further object of the present invention to provide a method of evaluating multiple external events to determine their public relations, advertising and marketing efforts and revenue significance.

It is a further object of the present invention to provide a relationship between data sets working from a finish-to-start methodology (Finish-Line Approach) to determine the relationship.

It is a further object of the present invention to provide a scalable, Software-as-a-Service (SaaS) solution that uses statistical modeling to establish relationships between any data. The system has the ability to digest any data, establish a mathematic relationship between the data, and identify the actions that the data was measured against to show which has the most quantitative impact on business, and finally, use this information to build a predictive model.

It is a further object of the present invention to provide this information dynamically in real-time.

Thus according to the principles of the invention, there is provided a method of doing business and a system for gathering a plurality of external promotional events having significance to a customer, indexing the external events for an electronic database and abstracting predefined portions thereof for inclusion in the database, evaluating the influence of each of the external references to the defined customer and generating at least one report summarizing the influence or return that each of the external events has on revenue.

Further features and advantages of the invention as well as the structure and operation of the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. In the drawings, like numbers indicate identical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings in which:

FIG. 1 is an exemplary block diagram that depicts the structure of the system embodying the present invention.

FIG. 2 is an exemplary graphical representation that depicts the results of the present invention's analysis of the relationship between several external variables and revenue.

FIG. 3 is an exemplary graphical representation of the flow of information and decision making of the present invention.

FIG. 4 is an exemplary graphical representation of the iterative process in determining the Dynamic Sphere of Influence.

FIG. 5 is a graphical representation showing the Software flow of information from a User standpoint.

FIG. 6 is a graphical representation showing the detail of the flow of information in the Statistic Engine operation.

DETAILED DESCRIPTION OF THE INVENTION

The following exemplary description employs a sample of the data sets from the automotive industry that may be utilized in the present invention: (i) media car loans, (ii) story requests, (iii) press/marketing events, (iv) published/advertising content, (v) PR/marketing spend, (vi) key performance indicators (such as car sales) and (vii) a custom field. It is then described how these options are combined and packaged to provide the graphical result. The examples provided merely represent one possible embodiment of the invented method, and different embodiments of the discussed concepts are easily conceivable.

Media Car Loans: Car manufacturers provide automobiles to the media for test drives and pre-release evaluation.

Story Requests: Media queries to car manufactures regarding interest to publish stories.

Press/Marketing Events: Media or consumer invited to events sponsored by car manufacturers for new product introduction and tests.

Published/Advertising Content: Media coverage or advertising on TV, in magazines, on Internet, or in any other emerging media such as social networks.

PR/Marketing Spend: The cost associated with PR and marketing levers.

Key Performance Indicators (KPI): Important metrics describing the ultimate company goals determined and selected by the software user. Examples: car sales, unit sales, etc.

Custom: Placeholder for one or more Key Performance Indicators (KPI) such as share-of-voice, conquests buys, brand awareness or reputation measures common to PR and marketing functions.

With respect to FIG. 1, system 100 of the present invention is utilized as follows: a query is sent via a User Interface 102 using the Graphical User Interface (GUI) module 104 for input. Relevant information from a plurality of databases 106 a, 106 b, 106 c and 106 d, will be assessed and collected through a data ingestion module 108. This information, along with information from the existing database 110 is sorted in the Indexing Engine 112 and forwarded to the Data Query Engine 114. The data is then sent to the Proprietary Methodology module 120. Information is then split between the Graphical Engine 122 and the Statistical Engine 124 and modified as necessary. The information may then be graphically represented through the Visualization module 126, compared to other data or data sets in the Comparative module 128 or used for assessing future performance or effect in the Predictive module 130. The results are then sent through a Decision Engine module 132 and a suggested course of action given by the Recommended Actions module 134. Alternatively, just the existence of the relationship between variables may be desired in which case the results are presented through the Data Discovery module 136. The results from either, or both, track(s) are then fed through the GUI Output module 138 back to the User Interface 102.

With respect to FIG. 2, the Output GUI 138 of FIG. 1 may be represented in the manner of the system screen 200. Function Bar 210 contains multiple functions that may be selected through an input device, for example, a mouse, a pen, a touch screen or by voice command. One embodiment of the present invention includes a New/Open Analytics tab, a Custom Data tab, Standard Editing Functions, Benchmark Data Configuration, Standard/Favorite Analytic Scenarios, Share/Publish Report and Help Functions. Additional or alternative functions may be configured as necessary or appropriate for different embodiments. A Filter or Search by Keyword Box 212 allows a user to search for particular data, results or other elements of the database by text, keyword or other search strings. Both the Search and Filter function may be contextual, literal or employ Boolean arguments to generate results. Display Tabs 230 allow the user to select the type of result representation that he or she wishes to have displayed.

In one embodiment of the present invention a Trend Analysis tab shows the relationship between several plotted variables in a graphical output display. There are additional Data Discovery, Action Items and PR/Marketing Data Configuration tabs also shown. These tabs are exemplary and are not to be considered limiting.

Relationship Variables 240 are also designated on the screen 200. In one embodiment of the present invention, Media Car Loan, Story Request, Press/Marketing Event, Published/Advertising Content, PR/Marketing Spend, Key Performance Indicators and Custom Tracking tabs are available. As with the Display Tabs above, these categories are exemplary and not to be considered limiting. A Time Slider tab 242 is available to scroll chronologically through a display showing the effect of differing media types sequentially over time. A Custom Date range 244 option is also available to display results between a specific start and end date. Tabs are also available to select between PR/Marketing Actions 246 and Correlation Benchmark 248. The current selections of correlation factors are displayed in a Correlation Factor window 250. The current selection of data being analyzed and their associated time shift against a correlation benchmark are displayed in the Correlation Shift window 252. A Dynamic Step Display tab 254 allows the selection of a movable Dynamic Time Step 256. The Time Unit selection 258 allows the user to change the units of time portrayed on the output. An Exact Value window 260 allows for the selection of the exact contribution of any one of the Relationship Variables 240.

With respect to FIG. 3, a graphical representation of the “Finish-Line Approach” system 300 of the present invention is exemplified. The Customer Defined KPI (Key Performance Indicator) (End Goal) 302 is selected. The KPI is used as a Benchmark/Filter for Data Analysis 304. The data set or sets are then entered into the Proprietary Methodology module 120 along with Upstream Measured Data A 306 a, Upstream Measured Data B 306 b, Upstream Measured Data C 306 c, Upstream Measured Data N 306 d. The output of the Proprietary Methodology module 120 is then analyzed to determine if a Statistical Relationship 308 exists. If No, then More Data and Analysis Needed 310 is flagged and additional Upstream Measured Data 360 a, 306 b, 306 c, 306 d is required. If Yes, then Identified Influence of Measured Data per Action on KPI is flagged and the Test Influence 314 is considered. If the result of Test Influence 314 is Yes, More Data and Analysis 310 is flagged. If Test Influence is No, Influence of Measured Data per Action on KPI 316 is confirmed and Action Operational Levers which Drive KPI are Identified 318. This may be iteratively applied back through the Customer Defined KPI (End Goal) module 302 for continuous improvement.

With respect to FIG. 4, a graphical representation of the Sphere of Influence (SOI) Index system 400 is depicted. The customer identifies an SOI Relevant to a Business Function 402, that data is entered into the “Finish-Line Approach” system 300. If an Actionable Operational Lever is Identified that Drives KPI 318, the result is used to Determine the Number of Influence Data to be included in the SOI Index 404. The data is then analyzed to Determine the Weighting of Each Influence Data to be included in the SOI Index 406. A check is made to determine if there is a Real-Time Update for the SOI 408. If Yes, the data is checked for Test Influence 314 in the “Finish-Line Approach” system 300. If No, an SOI Index Snapshot 410 is created.

With respect to FIG. 5, the Proprietary Software System 500 includes a User Login 502 and an Authentication Module 504. Upon authentication, the user may Select Data for Analysis 506. The system may query if the selected data is New Data 508. If the data is new, the user may choose a Data File and Type 510, Upload the Data File 512 and the system will Parse the Data File and Load the File into Database 514.

If the data is already resident in the database, either one or both of two paths, Statistical or Graphical, may be pursued. In the first, the Statistical Engine 516 performs visualization, comparative and predictive functions on the data. The results may then be sent to a Data Discovery module 518 which will correlate such visualized, comparative and predictive relationships between the data. The relationships determined by the Data Discovery module 518 will then be relayed to the user as Recommended Actions 524.

Alternatively, or in concert to the above data manipulation, the Graphical Engine 520 performs visualization, comparative and predictive functions on the data. The results may then be sent to a Decision Engine 522 which will correlate such visualized, comparative and predictive relationships between the data. The results correlated by the Decision Engine 522 will then be relayed to the user as Recommended Actions 524. The Recommended Actions will be presented to the user through a Graphical User Interface 526 and may be saved, transmitted, printed or deleted. The user may then exit the system through the User Logout 528.

If the Authentication 504 fails, the user may be prompted to a Sign Up/Password Retrieval module 530. If the user has an account does not wish to create a new account, they will be sent a Password Recovery Email 534 and returned to the User Login 502.

If the user wishes to sign up, they may do so and a Sign Up Confirmation Email 536 will be generated and sent to the user, who is then returned to the User Login 502.

With respect to FIG. 6, the Statistical Engine system 600, is shown in detail. The Statistical Engine system comprises a Data Input module 602 which transfers data to the Data Preparation module 604. The Data Preparation module 604 presents the data to the program in a computer readable format. Upon such presentation, the data is validated at the New Data module 606. If yes, then a Predictive Recognition Verification is performed with Updated Realtime Data 608. The Predictive Model 610 is verified then the predictive model is sent to Data Discovery in FIG. 1, module 136 and Decision Engine in FIG. 2, module 132. If Predictive Model 610 fails, additional analysis will be performed with modules 612, 624 and 636 until predictive recognition is completed and verified, and with updated real-time data.

If the data is verified in New Data module 606, a series of data manipulation and analyses are performed. The first step is a Data Pattern Recognition 612. Analysis executed here may include existing pattern recognition math techniques, but not limited to descriptive statistics, correlation, time series, etc. If any there are no desired relationships within or between the data at the Patterns of Interest module 622, the data is examined again, starting with Data Pattern Recognition 612.

If a positive result is returned from the Patterns of Interest module 622, the patterns are forwarded to a Data Relationship Recognition module 624. A further series of data manipulation and analyses are applied to the patterns starting with existing relationship recognition math techniques, but not limited to factor, cluster, boot-strapping analysis, etc. The result of these analyses and manipulation is intended to be a weighting of the result. If the Weighting Established Module 634 is triggered negatively, the relationships are examined iteratively in step 624 again.

If the Weighting Established Module 634 returns a positive result, the data is fed into a Data Predictive Recognition Module 636. Additional analyses may be implemented to support the predictive behavior including existing predictive recognition math techniques, but not limited to regression, discriminate function, etc. If the Predictive Established module returns a negative result, the system may be reset to the Data Input Module 602 for additional data to be entered.

If the Predictive Established Module 644 returns a positive result then a Predictive Recognition Verification is performed with Updated Realtime Data 608.

While various embodiments of the disclosed system, software, and method have been described above, it should be understood that they have been presented by way of example only, and should not limit the claimed invention. Likewise, the various diagrams may depict an example architectural or other configuration for the disclosed system, software, and method. This is done to aid in understanding the features and functionality that can be included in the disclosed system, software, and method. The claimed invention is not restricted to the illustrated example architectures or configurations, rather the desired features can be implemented using a variety of alternative architectures and configurations. Indeed, it will be apparent to one of skill in the art how alternative functional, logical or physical partitioning and configurations can be implemented to implement the desired features of the disclosed system, software, and method. Also, a multitude of different constituent module names other than those depicted herein can be applied to the various partitions. Additionally, with regard to flow diagrams, operational descriptions and system or method claims, the order in which the steps are presented herein shall not mandate that various embodiments be implemented to perform the recited functionality in the same order unless the context dictates otherwise.

Although the disclosed system, software, and method is described above in terms of various exemplary embodiments and implementations, it should be understood that the various features, aspects and functionality described in one or more of the individual embodiments are not limited in their applicability to the particular embodiment with which they are described. Thus, the breadth and scope of the claimed invention should not be limited by any of the above-described exemplary embodiments.

Terms and phrases used in this document, and variations thereof, unless otherwise expressly stated, should be construed as open ended as opposed to limiting. As examples of the foregoing: the term “including” should be read as meaning “including, without limitation” or the like; the term “example” is used to provide exemplary instances of the item in discussion, not an exhaustive or limiting list thereof; the terms “a” or “an” should be read as meaning “at least one,” “one or more” or the like; and adjectives such as “conventional,” “traditional,” “normal,” “standard,” “known” and terms of similar meaning should not be construed as limiting the item described to a given time period or to an item available as of a given time, but instead should be read to encompass conventional, traditional, normal, or standard technologies that may be available or known now or at any time in the future. Likewise, where this document refers to technologies that would be apparent or known to one of ordinary skill in the art, such technologies encompass those apparent or known to the skilled artisan now or at any time in the future.

A group of items linked with the conjunction “and” should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as “and/or” unless expressly stated otherwise. Similarly, a group of items linked with the conjunction “or” should not be read as requiring mutual exclusivity among that group, but rather should also be read as “and/or” unless expressly stated otherwise. Furthermore, although items, elements or components of the disclosed method and apparatus may be described or claimed in the singular, the plural is contemplated to be within the scope thereof unless limitation to the singular is explicitly stated.

The presence of broadening words and phrases such as “one or more,” “at least,” “but not limited to” or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent. The use of the term “module” does not imply that the components or functionality described or claimed as part of the module are all configured in a common package. Indeed, any or all of the various components of a module, whether control logic or other components, can be combined in a single package or separately maintained and can further be distributed in multiple groupings or packages or across multiple locations.

Additionally, the various embodiments set forth herein are described in terms of exemplary block diagrams, flow charts and other illustrations. As will become apparent to one of ordinary skill in the art after reading this document, the illustrated embodiments and their various alternatives can be implemented without confinement to the illustrated examples. For example, block diagrams and their accompanying description should not be construed as mandating a particular architecture or configuration. 

1. A method of providing a relationship between sets of data comprising: a) gathering a first data set with at least one first data string, b) gathering a second data set with at least one second data string, c) relating the at least one first data string to the at least one second data string with a quantitative value, d) determining the at least one first data string's weighting and the at least one second data string's weighting to a benchmarked data, e) constructing a predictive mathematical model of the relationship between the at least one first data string's and the at least one second data string's behavior against a benchmarked data, f) constructing a graphical representation of the relationship between the at least one first data string's and the at least one second data string's behavior against a benchmarked data, g) constructing a predictive model of the at least one first data string; and h) displaying the graphical representation on a user interface.
 2. The method of providing a relationship between sets of data according to claim 1 wherein the at least one first data set is marketing data.
 3. The method of providing a relationship between sets of data according to claim 1 wherein the at least one first data set is public relations data.
 4. The method of providing a relationship between sets of data according to claim 1 wherein the at least one first data set is advertising data.
 5. A system for evaluating the relation between sets of data comprising: a) a first data set with at least one first data string, b) a second data set with at least one second data string, c) a means for relating the at least one first data string to the at least one second data string with a quantitative value, d) a means for determining the at least one first data string's weighting and the at least one second data string's weighting to a benchmarked data, e) a means for constructing a predictive regression model of the relationship between the at least one first data string, f) a means for constructing a graphical representation of the relationship between the at least one first data string's and the at least one second data string's behavior against a benchmarked data; and g) a user interface for displaying the graphical representation.
 6. The system for evaluating the relation between sets of data according to claim 5 wherein the system is presented on as a computer program.
 7. The system for evaluating the relation between sets of data according to claim 5 wherein the system is presented on as software as a service.
 8. The system for evaluating the relation between sets of data according to claim 5 wherein the at least one first data string is marketing data.
 9. The system for evaluating the relation between sets of data according to claim 5 wherein the at least one first data string is public relations data.
 10. The system for evaluating the relation between sets of data according to claim 5 wherein the at least one first data string is advertising data.
 11. A system for providing an attribution of relevance to compiled data comprising: a) a means for gathering a plurality of data having significance for a defined customer, b) a means for compiling the data in an electronic database, c) a means for evaluating the value of each of the data to the defined customer in the electronic database, d) a means for defining a set of variables affected by the value of the significant data e) a means for assigning a weighted value to the defined set of variables, f) a means for constructing a predictive model based on the defined set of variables, g) a means for generating at least one report graphically for the defined customer using the electronic database summarizing a plurality of the data to provide an indicator of a relationship between the plurality of data to the defined customer; and h) a user interface to display the at least one report.
 12. The system for providing an attribution of relevance to compiled data of claim 11 wherein the compiled data is marketing data.
 13. The system for providing an attribution of relevance to compiled data of claim 11 wherein the compiled data is public relations data.
 14. The system for providing an attribution of relevance to compiled data of claim 11 wherein the compiled data is advertising data. 